def fetch_attention_feats(interpreter, text):
msg = Message({TEXT: text})
for p in interpreter.interpreter.pipeline:
p.process(msg)
diag_data = msg.as_dict()["diagnostic_data"]
diet_key = [k for k in diag_data.keys() if "DIETClassifier" in k][0]
return (
diag_data[diet_key]["attention_weights"],
[t.text for t in msg.as_dict()["text_tokens"]] + ["<SENT>"],
)
def parse(
self,
text: Text,
time: Optional[datetime.datetime] = None,
only_output_properties: bool = True,
) -> Dict[Text, Any]:
"""Parse the input text, classify it and return pipeline result.
The pipeline result usually contains intent and entities."""
if not text:
# Not all components are able to handle empty strings. So we need
# to prevent that... This default return will not contain all
# output attributes of all components, but in the end, no one
# should pass an empty string in the first place.
output = self.default_output_attributes()
output["text"] = ""
return output
data = self.default_output_attributes()
data[TEXT] = text
message = Message(data=data, time=time)
for component in self.pipeline:
component.process(message, **self.context)
output = self.default_output_attributes()
output.update(
message.as_dict(only_output_properties=only_output_properties))
return output
